alphafold

name	alphafold
description	Use when running AlphaFold2 predictions on custom protein sequences, validating designed sequences via self-consistency, predicting binder-target complexes, or interpreting AF2 confidence metrics (pLDDT, pTM, ipTM).
metadata	null

AlphaFold2 Structure Prediction

Use when the agent needs to run AlphaFold2 for protein structure prediction and complex modeling. Covers validating designed sequences, predicting binder-target complexes, and calculating confidence metrics (pLDDT, pTM, ipTM).

Distinct from alphafold-database (which retrieves pre-computed structures) — this skill covers running AF2 predictions on custom sequences.

Requirements

• Python 3.8+
• CUDA 11.0+, 32 GB GPU VRAM minimum (A100 recommended)
• For multimers: ColabFold recommended over local install

Deployment Options

1. ColabFold (Recommended for Multimers)

pip install colabfold[alphafold]

# Single chain
colabfold_batch input.fasta output_dir/ \
    --model-type alphafold2_ptm \
    --num-recycles 3

# Complex (multimer) — comma-separate chains in FASTA header
# >complex:ChainA,ChainB
colabfold_batch complex.fasta output_dir/ \
    --model-type alphafold2_multimer_v3 \
    --num-recycles 20 \
    --num-models 5

2. LocalColabFold

# Install
wget https://raw.githubusercontent.com/YoshitakaMo/localcolabfold/main/install_colabbatch_linux.sh
bash install_colabbatch_linux.sh

# Run offline
colabfold_batch sequences.fasta results/ \
    --model-type alphafold2_multimer_v3 \
    --num-recycles 3 \
    --use-gpu-relax

3. OpenFold (PyTorch reimplementation)

pip install openfold
python run_pretrained_openfold.py \
    --fasta_paths input.fasta \
    --output_dir results/ \
    --model_device cuda:0

Key Parameters

Parameter	Values	Notes
--model-type	alphafold2_ptm, alphafold2_multimer_v3	Use multimer for complexes
--num-recycles	3–20	More recycles = better accuracy, slower
--num-models	1–5	5 models for ensemble confidence
--msa-mode	mmseqs2_uniref_env (default), single_sequence	Single = no MSA, faster
--use-gpu-relax	flag	Amber relaxation on GPU

Confidence Metrics

import numpy as np
import json

# Load result JSON
with open("result_model_1.json") as f:
    result = json.load(f)

plddt = np.array(result["plddt"])           # Per-residue confidence 0-100
ptm = result["ptm"]                          # Global TM-score estimate 0-1
iptm = result.get("iptm", None)             # Interface TM-score (multimer only)
pae = np.array(result.get("pae", []))       # Predicted Aligned Error matrix

# Quality thresholds
print(f"Mean pLDDT: {plddt.mean():.1f}")    # >70 = good, >90 = excellent
print(f"pTM: {ptm:.3f}")                    # >0.5 = confident fold
if iptm:
    print(f"ipTM: {iptm:.3f}")              # >0.6 = reliable complex, >0.8 = high confidence

Self-Consistency Validation for Designed Sequences

# Design → predict → measure similarity to input backbone
# 1. Generate sequences with ProteinMPNN
# 2. Predict structure of each sequence with AF2
# 3. Calculate TM-score / RMSD vs. design backbone

python3 -c "
from Bio.PDB import PDBParser, Superimposer
# Compare predicted vs. designed structure
# High TM-score (>0.8) = sequence encodes target fold
"

Output Files

File	Contents
*_relaxed_rank_1.pdb	Top-ranked relaxed structure
*_unrelaxed_rank_1.pdb	Top-ranked unrelaxed structure
result_model_*.json	Scores: pLDDT, pTM, ipTM, PAE matrix
*_coverage.png	MSA coverage plot
*_pae.png	PAE heatmap (low = confident)

Quality Thresholds

Metric	Poor	Acceptable	Good	Excellent
Mean pLDDT	<50	50–70	70–90	>90
pTM	<0.4	0.4–0.5	0.5–0.7	>0.7
ipTM (complex)	<0.5	0.5–0.6	0.6–0.8	>0.8
Interface PAE	>20 Å	15–20 Å	8–15 Å	<8 Å

Common Issues

Problem	Cause	Fix
Low ipTM despite high pLDDT	Chains fold well independently but don't interact	Redesign interface residues
High PAE at interface	Interface not well-determined	Add more recycles; check contact predictions
OOM on GPU	Sequence too long	Use --chunk-size 128 or CPU for MSA
All models disagree	Disordered region or wrong fold	Check MSA depth; try --msa-mode single_sequence